{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the NEP results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                Step[ FC]     Time          Energy          fmax\n",
      "BFGSLineSearch:    0[  0] 16:57:16      -10.862692        0.0089\n",
      "                Step[ FC]     Time          Energy          fmax\n",
      "BFGSLineSearch:    0[  0] 16:57:21      -14.966002        0.0222\n",
      "BFGSLineSearch:    1[  2] 16:57:21      -14.966009        0.0000\n",
      "                Step[ FC]     Time          Energy          fmax\n",
      "BFGSLineSearch:    0[  0] 16:57:25      -12.907792        0.0229\n",
      "BFGSLineSearch:    1[  2] 16:57:25      -12.907796        0.0001\n",
      "                Step[ FC]     Time          Energy          fmax\n",
      "BFGSLineSearch:    0[  0] 16:57:30      -14.896897        0.1638\n",
      "BFGSLineSearch:    1[  2] 16:57:30      -14.897213        0.0046\n",
      "                Step[ FC]     Time          Energy          fmax\n",
      "BFGSLineSearch:    0[  0] 16:57:35      -21.672411        0.0418\n",
      "BFGSLineSearch:    1[  2] 16:57:35      -21.672426        0.0002\n",
      "                Step[ FC]     Time          Energy          fmax\n",
      "BFGSLineSearch:    0[  0] 16:57:40      -14.224957        0.1471\n",
      "BFGSLineSearch:    1[  2] 16:57:40      -14.225294        0.0019\n",
      "                Step[ FC]     Time          Energy          fmax\n",
      "BFGSLineSearch:    0[  0] 16:57:44      -20.876577        0.0440\n",
      "BFGSLineSearch:    1[  2] 16:57:44      -20.876592        0.0000\n",
      "                Step[ FC]     Time          Energy          fmax\n",
      "BFGSLineSearch:    0[  0] 16:57:49      -24.385720        0.0309\n",
      "BFGSLineSearch:    1[  2] 16:57:49      -24.385725        0.0001\n",
      "                Step[ FC]     Time          Energy          fmax\n",
      "BFGSLineSearch:    0[  0] 16:57:54      -19.041128        0.1482\n",
      "BFGSLineSearch:    1[  2] 16:57:54      -19.041405        0.0003\n",
      "                Step[ FC]     Time          Energy          fmax\n",
      "BFGSLineSearch:    0[  0] 16:57:59      -21.878983        0.2572\n",
      "BFGSLineSearch:    1[  2] 16:57:59      -21.879611        0.0071\n",
      "                Step[ FC]     Time          Energy          fmax\n",
      "BFGSLineSearch:    0[  0] 16:58:04      -23.639722        0.0019\n",
      "                Step[ FC]     Time          Energy          fmax\n",
      "BFGSLineSearch:    0[  0] 16:58:09      -18.008956        0.0963\n",
      "BFGSLineSearch:    1[  2] 16:58:09      -18.009099        0.0009\n",
      "                Step[ FC]     Time          Energy          fmax\n",
      "BFGSLineSearch:    0[  0] 16:58:14      -25.915146        0.1450\n",
      "BFGSLineSearch:    1[  2] 16:58:14      -25.915317        0.0025\n",
      "                Step[ FC]     Time          Energy          fmax\n",
      "BFGSLineSearch:    0[  0] 16:58:19       -3.012651        0.0467\n",
      "BFGSLineSearch:    1[  2] 16:58:19       -3.012752        0.0000\n",
      "                Step[ FC]     Time          Energy          fmax\n",
      "BFGSLineSearch:    0[  0] 16:58:24      -15.661939        0.0981\n",
      "BFGSLineSearch:    1[  2] 16:58:24      -15.662138        0.0008\n",
      "                Step[ FC]     Time          Energy          fmax\n",
      "BFGSLineSearch:    0[  0] 16:58:29      -17.023513        0.2059\n",
      "BFGSLineSearch:    1[  2] 16:58:29      -17.024306        0.0005\n"
     ]
    }
   ],
   "source": [
    "from wizard.atoms import SymbolInfo\n",
    "from wizard.calculator import MaterialCalculator\n",
    "from calorine.calculators import CPUNEP\n",
    "\n",
    "calc = CPUNEP('../potentials/4-4-80/nep.txt')\n",
    "nep_data = {}\n",
    "\n",
    "symbol_infos = [\n",
    "SymbolInfo('Ag', 'fcc', 4.146),\n",
    "SymbolInfo('Al', 'fcc', 4.042),\n",
    "SymbolInfo('Au', 'fcc', 4.159),\n",
    "SymbolInfo('Cu', 'fcc', 3.631),\n",
    "SymbolInfo('Ni', 'fcc', 3.509),\n",
    "SymbolInfo('Pb', 'fcc', 5.038),\n",
    "SymbolInfo('Pd', 'fcc', 3.939),\n",
    "SymbolInfo('Pt', 'fcc', 3.967),\n",
    "SymbolInfo('Cr', 'bcc', 2.845),\n",
    "SymbolInfo('Mo', 'bcc', 3.164),\n",
    "SymbolInfo('Ta', 'bcc', 3.319),\n",
    "SymbolInfo('V',  'bcc', 2.997),\n",
    "SymbolInfo('W',  'bcc', 3.185),\n",
    "SymbolInfo('Mg', 'hcp', 3.195, 5.186),\n",
    "SymbolInfo('Ti', 'hcp', 2.931, 4.651),\n",
    "SymbolInfo('Zr', 'hcp', 3.240, 5.157)\n",
    "]\n",
    "\n",
    "for symbol_info in symbol_infos:\n",
    "    atoms = symbol_info.create_bulk_atoms((1,1,1))\n",
    "    material_calculator = MaterialCalculator(atoms, calc, symbol_info)\n",
    "    Cij = material_calculator.elastic_constant()\n",
    "    nep_data[symbol_info.formula] = {}\n",
    "    nep_data[symbol_info.formula]['C11'] = Cij[0][0]\n",
    "    nep_data[symbol_info.formula]['C12'] = Cij[0][1]\n",
    "    nep_data[symbol_info.formula]['C13'] = Cij[0][2]\n",
    "    nep_data[symbol_info.formula]['C33'] = Cij[2][2]\n",
    "    nep_data[symbol_info.formula]['C44'] = Cij[3][3]\n",
    "    nep_data[symbol_info.formula]['C66'] = Cij[5][5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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sW7bM6VgABRPf1+TijpCLZ5SVXPxqt9xyi5Fk2rZtm+2xxpCLk4uTiztCLo6soBAOj3Ts2DH7vGR9+/bN1bpWrlyZbk61oKAg+18dJZmiRYua1atXZxiX9stg7ty59nWEhIQYHx8fe1vz5s1NcnKy+fbbb+1/2Q8NDU03B1y3bt0cxmb7gh4yZIhp3ry5ka7MIXf1HHDDhw93ON72JWsbFx4ebj+DQboyj92cOXMyHfvwww+bzp072/9qW7RoUVOoUKEsJd9btmyxJ2HSlTnyrp7/zpYApHXo0CFz88032/v4+fmZ0NBQ+8+FChUy77//vsO4bcn3Bx98YE9U/fz8THBwsH28xWIxkyZNcjj+WuLi4kxUVFS6fbp6LsDBgwenGzNr1ixTokSJdM9FiRIl7I/7778/S9v++eef7eNHjRqVo/idcWUhfPv27fY433rrrWyN5b2dtfd2UlKS6datW7r3hO29aVvWvXt3h8l/2uT7ySeftI+/etsPP/yw0+d22rRpxmq1pvssCQwMtP9cpEgR88MPP2QYlzb5nj59uj0JDg0NTRd7zZo1zblz5+zjDhw4YEqUKGF//WyJYdrHrFmznMZ7NdtnWuXKlTPt16RJEyPJNGnSJMM+2D5LQkND7a+/7TFo0CCH63N38r1582ZTtmzZdJ9PRYoUSRfva6+9lmHcwYMHTfny5TMcT2mPd1f9kQyAd+D7mlycXNy1uXhax48ftx+rTz75ZLbHk4uTi5OLk4sj5yiEwyPNnDnT/oHw7bff5ng9Bw4csH/h1KhRw6xZs8betmrVKlOtWjUjyYSHh2e4bCftF3RYWJi58847ze+//26MMebChQvm/ffft39wDR061ISGhppu3bqZffv2GWOMOXfunHnppZfs61i6dGmG+Gxf0KGhocZqtZqPP/7YXLx40R57ly5d7OMXLFiQYfyoUaPMW2+9ZX777Tdz6dIlY8yVMxK2b99uevToYU9IDh8+nGGs7Qs6ODjY+Pr6mrffftucPXvWHvuRI0cyPA9XJ9933nmnkWTq1atnYmNj7WcJJCUlmd27d5u3337bvPnmm+nGXL582Z40h4aGmmnTptlvvPLXX3+Ze++91/4l4uivvbbku2jRoqZMmTJm/vz59gRk586dplGjRvb9utaljI7YvrT9/f3N+++/bxISEowxV8526NOnj/25+OijjzKMze2lWGPGjLGPz+wMopzIbSE8OTnZHDx40EyZMsX+JR4eHp7lM2VseG9n7b09aNAg+/tg2LBh9ktRT58+bf773//ax77wwgsZxtpea9sZCmPHjrW/t0+ePGn69etnH798+fIM45csWWIKFSpkfH19zfPPP2/27t1rUlNTTWpqqtm5c6fp2rWr/ZeVq89GsSWuhQsXNlar1fTr188cOHDAGHPl7I7x48fbE/Jhw4Zl2LYrLsc0xpjvvvvOvo8//fSTwz47d+6090n7y/r8+fPN4MGDzZo1a+zvf2OMOXLkiBk5cqQ9fkevmzuT7yNHjpjixYsb6coNq3799Vf7Z98///xjhg0bZi++XH3zrL59+xpJpmLFimbZsmX2qZMuX75s9u3bZz766COHxxKAgovva3JxcnHX5uKXL182R44cMXPnzjW1atWyFxt37tyZpfHk4uTi5OLk4nANCuHwSEOHDrV/ADlKHLOqf//+9i+ho0ePZmg/ePCgfX60q/8an/YLumbNmiYxMTHD+F69etn7tG7d2uHlYra/QDv6i7vtC/rqD3+blJQUc/vtt9tjyK527doZyfEZDWnPYHF2xocxmSfftr9KZ3YJ0NVmzZplX5+jv2JfunTJnpzffPPNGdptybfVarVfPpXW8ePHTUBAgJFkpk2bluW4jDFm/fr19tgmTpzosI8tOY+MjLQnUza5Tb579uxp37ecXGKZmZwWwm1J7NWPGjVqmK1bt2Y7Dt7bV2T23j506JA9iRoyZIjD/R84cKD9FyjbL8o2ad/bzi5hvPXWW42UcV7KlJQUU6VKlUzfA8YYc9999xlJ5plnnkm3PO0ZHI7OQEsbu6MzRFyVfKekpNjP1nAWx/PPP2//RT3tGTHX8tZbbxlJ5s4778zQ5s7k2/bL/0MPPeR0/NixY40kU6dOnXTLq1evbiSZGTNmON8xAEiD7+sryMXJxXPrrrvucphLlytXzvz444/XHE8uTi7uCLk4uThyrpAAD3Tq1Cn7/8PDw3O0DmOMvv76a0lS//79VbJkyQx9ypYtq/79+0uSZs2a5XRdzz77rKxWa4bld911l/3/L774osM7k9v6bNu2zen6y5Urp969e2dYXqhQIQ0dOlSS9Pvvv+u3335zug5HbHddXrNmjdM+RYsW1WOPPZat9dqEhYVJko4ePZrlMV999ZUkqXHjxmrTpk2Gdl9fXw0fPlyStH37dqf73KVLF910000ZlhcrVkyNGzeWlPlznllsZcuWVb9+/Rz2GTVqlCTp5MmTWrp0abbWfy224z4sLEyFCnnGx3OxYsVUokQJhYSE2JfVrl1bH3zwgWrXrp3t9fHeviKz9/bcuXN1+fJlBQQE6MUXX3S43qFDh8pqterSpUuaM2eO021HR0c7bLvvvvscxr5q1Srt2bNHkZGRTt8DkvTwww9Lkn744QenfWz7d7UOHTpIkv78809duHDB6fjcKFSokGJiYiRJc+bM0fnz59O1p6Sk6Msvv5QkPfDAAwoODs7yum2fq7GxsUpJSXFNwNeQmJioGTNmSJJeeOEFp/1sr8vWrVv1zz//2Jfn5LMaQMHG9/UV5OLk4rkVHh6uEiVKqGjRovZlFSpU0Pvvv68WLVpcczy5OLm4I+Ti5OLIOc+otABusHfvXp0+fVqS1KpVK6f9WrduLelKUrB3716HfRo0aOBweYkSJez/r1+/fqZ9zpw54zSGqKgoh1/uktS8eXP5+vpKkn799dcM7Vu3btUTTzyh2rVrKyQkRIUKFZLFYpHFYtETTzwhSTp06JDTbdevX1/+/v5O2zNz7733SpKio6M1aNAg/fTTT9f8MrXtQ2avScuWLeXj45Ou/9UaNmzodHzp0qUlyf76Z5VtWy1btnSa/FavXl1lypTJNDZvsnr1ah07dkxnz57VqVOnNGnSJJ06dUp33nmn+vbtq8uXL+d5TN7+3rb9v379+ul+6UmraNGiuu222zKMTat+/fpOt+3sPbJ27VpJ0tmzZ1W6dGmVLFnS4eORRx6RJO3fv9/h+sPDw1W5cuVMty1l/tzlVu/evWWxWJSQkGD/xdrm+++/tyeiffv2zTD2n3/+0fDhw9W4cWNFRETI19fX/rlao0YNSdKFCxfcGn9aGzduVGJioiSpTZs2Tl+XmjVr2sekfW1sn9UvvviiHn30US1evFjx8fF5EjuAgsvbv69tyMUzIhf/14wZM3Ts2DGdPn1a8fHxmjt3rgICAnT//ferffv2GQqEVyMXJxcnFycXh2tRCIdHioiIsP8/uwmUzfHjx+3/tyVLjpQtW9bhmLSKFCnicLntizMrfS5duuQ0hsziCwgIsD8fV8c3fvx41atXTx999JF+++03nT9/XqGhoSpRokS6MwcSEhKcrr948eJO267lzTffVMuWLXX+/HmNHTtWUVFRCgkJ0W233abhw4fr8OHDGcbY9uFa+xwZGZmu/9WcPd9S1p5zR7ISm/TvMeMstpyyvc5xcXFKTU116bpdITw8XH369NHatWsVHByszz//XB9//HG21sF7+1/O3tuuOg5z8h45cuSIffk///zj9GFLOi9evJjjbTvavivdcMMNioqKkiR9/vnn6dpsP990001q0qRJurbY2FjddNNNeuWVV7R+/XqdPn1agYGBKl68uEqUKGH/bJIy/2x1JdvrIinT1yXtmSdpCyHPPfecHnjgAV26dEmffvqp7r77boWFhalWrVp67rnntGvXrjzZDwDXD76v/0UuTi7uKkWKFFGnTp20fv16lStXTt99951GjBiR5fHk4o77kIvnbNuOtu9K5OLk4p6KQjg8Utq/pG3evDkfI/FcO3bs0IABA5SamqquXbvq559/VmJios6cOaNjx47p2LFjGjt2rKQrl685YzvbIyfCwsL0448/avXq1Xr++efVtGlT+fr6auPGjXrllVdUpUoVzZw5M8frL2hsx31SUpJ27NiRz9E4V6FCBXXq1EmS9Mknn2RrLO9tz2a7vLBhw4YyV+4jcs2HJ7OdYbJu3Trt3r1bknTixAl9++23kqQ+ffqk63/58mV1795dcXFxqlu3rr777jvFx8fr3Llz+ueff3Ts2DGtX7/e3j+v9j/tZZ8XL17M0uti+8VDkvz8/PTVV19py5Ytevnll3XHHXeocOHC2r59u95++23VrFlT77zzTp7sC4DrA9/X10Yu7n3yKhcPCwuzT9fx6aefZns8ubj3IhcnF4f7UQiHR0p7Odw333yTo3WkPbsis8sR07bl5oyM3HB0toZNUlKSfS63tPHNmTNHKSkpql69umbNmuXwsspjx465J+CrNGvWTG+88YbWrFmjuLg4LViwQLVq1dLFixfVp0+fdH8Zte1DZq9JYmKiw312t6zElrbd1bHdeeed9v/n9LjPK7YzJP78889sjeO9/S9n7+38PA5tczw6u8zyetO5c2f7nHy2M0+mTZumS5cuydfX1z6Pn01sbKz2798vHx8fffvtt7r77rsznFGT089V29k3tssqHTl79qzD5Wnn3szNa1OnTh2NHDlSy5cvV1xcnJYtW6bbb79dKSkpeu6557R169YcrxuAd+H7+l/k4uTi7mDLpePj43N0Zju5eNaQi+cvcvH0yMU9A4VweKQSJUqoc+fOkq7Mq2b762FW2P4qWKlSJfsNQJYvX+60/7JlyyRduUysUqVKOQ05V3766Senf81cvXq1fe432zxkknTw4EFJVz5Mnc2hZ9u3vBQQEKD77rtP8+bNk3TliybtDYJs+5DZa7Jy5Ur7PjubE84dbLG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",
      "text/plain": [
       "<Figure size 1500x1500 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "dft_data = {\n",
    "    \"Ag\": {\"C11\": 100, \"C12\": 82, \"C44\": 41},\n",
    "    \"Al\": {\"C11\": 104, \"C12\": 73, \"C44\": 32},\n",
    "    \"Au\": {\"C11\": 144, \"C12\": 134, \"C44\": 29},\n",
    "    \"Cu\": {\"C11\": 180, \"C12\": 127, \"C44\": 78},\n",
    "    \"Ni\": {\"C11\": 276, \"C12\": 159, \"C44\": 132},\n",
    "    \"Pb\": {\"C11\": 47,  \"C12\": 32,  \"C44\": 18},\n",
    "    \"Pd\": {\"C11\": 187, \"C12\": 147, \"C44\": 71},\n",
    "    \"Pt\": {\"C11\": 303, \"C12\": 220, \"C44\": 54},\n",
    "    \"Cr\": {\"C11\": 499, \"C12\": 139, \"C44\": 102},\n",
    "    \"Mo\": {\"C11\": 472, \"C12\": 158, \"C44\": 106},\n",
    "    \"Ta\": {\"C11\": 265, \"C12\": 158, \"C44\": 69},\n",
    "    \"V\":  {\"C11\": 276, \"C12\": 131, \"C44\": 16},\n",
    "    \"W\":  {\"C11\": 510, \"C12\": 201, \"C44\": 143},\n",
    "    \"Mg\": {\"C11\": 58,  \"C12\": 30,  \"C13\": 22,  \"C33\": 66,  \"C44\": 20,  \"C66\": 14},\n",
    "    \"Ti\": {\"C11\": 177, \"C12\": 83,  \"C13\": 76,  \"C33\": 191, \"C44\": 42,  \"C66\": 47},\n",
    "    \"Zr\": {\"C11\": 144, \"C12\": 65,  \"C13\": 67,  \"C33\": 162, \"C44\": 26,  \"C66\": 40},\n",
    "}\n",
    "\n",
    "eam_data = {\n",
    "    \"Ag\": {\"C11\": 124.7, \"C12\": 93.4,  \"C44\": 46.1},\n",
    "    \"Al\": {\"C11\": 106.5, \"C12\": 59.5,  \"C44\": 27.9},\n",
    "    \"Au\": {\"C11\": 186.7, \"C12\": 156.9, \"C44\": 42.1},\n",
    "    \"Cu\": {\"C11\": 170.0, \"C12\": 121.0, \"C44\": 75.5},\n",
    "    \"Ni\": {\"C11\": 245.6, \"C12\": 147.0, \"C44\": 124.2},\n",
    "    \"Pb\": {\"C11\": 49.6,  \"C12\": 42.1,  \"C44\": 15.0},\n",
    "    \"Pd\": {\"C11\": 233.9, \"C12\": 175.0, \"C44\": 71.1},\n",
    "    \"Pt\": {\"C11\": 346.4, \"C12\": 249.1, \"C44\": 75.8},\n",
    "    \"Cr\": {\"C11\": 304.6, \"C12\": 135.8, \"C44\": 122.8},\n",
    "    \"Mo\": {\"C11\": 455.2, \"C12\": 167.3, \"C44\": 113.2},\n",
    "    \"Ta\": {\"C11\": 262.0, \"C12\": 158.2, \"C44\": 82.4},\n",
    "    \"V\":  {\"C11\": 231.6, \"C12\": 119.8, \"C44\": 45.9},\n",
    "    \"W\":  {\"C11\": 520.9, \"C12\": 205.0, \"C44\": 160.9},\n",
    "    \"Mg\": {\"C11\": 54.1,  \"C12\": 30.0,  \"C13\": 20.5,  \"C33\": 67.7,  \"C44\": 13.3,  \"C66\": 12.0},\n",
    "    \"Ti\": {\"C11\": 135.3, \"C12\": 94.5,  \"C13\": 68.5,  \"C33\": 203.0, \"C44\": 30.3,  \"C66\": 20.4},\n",
    "    \"Zr\": {\"C11\": 118.3, \"C12\": 88.9,  \"C13\": 67.0,  \"C33\": 181.1, \"C44\": 24.5,  \"C66\": 14.7}\n",
    "}\n",
    "\n",
    "nep_data = {'Ag': {'C11': 109.6826206693371, 'C12': 81.65947717980816, 'C13': 81.65947717980822, 'C33': 109.68262066910249, 'C44': 41.88751771447134, 'C66': 41.88751771443144}, 'Al': {'C11': 121.26570665001827, 'C12': 54.001818446366, 'C13': 54.001818446409104, 'C33': 121.2657066499968, 'C44': 38.94942709225214, 'C66': 38.94942709226294}, 'Au': {'C11': 154.83366840266183, 'C12': 118.47098036483378, 'C13': 118.47098036481411, 'C33': 154.83366840271626, 'C44': 32.71690276591296, 'C66': 32.716902765977345}, 'Cu': {'C11': 175.41560755212248, 'C12': 128.3310037611278, 'C13': 128.33100376097866, 'C33': 175.4156075520554, 'C44': 78.79590404715361, 'C66': 78.7959040471835}, 'Ni': {'C11': 272.9553559930957, 'C12': 179.21301887289522, 'C13': 179.21301887289525, 'C33': 272.9553559931699, 'C44': 112.27669267655229, 'C66': 112.27669267642058}, 'Pb': {'C11': 49.01569746350186, 'C12': 36.3466711209183, 'C13': 36.3466711209742, 'C33': 49.01569746353264, 'C44': 15.038748415548401, 'C66': 15.038748415559583}, 'Pd': {'C11': 190.58190547964594, 'C12': 149.70930534036953, 'C13': 149.70930534032325, 'C33': 190.5819054795354, 'C44': 71.8857594858142, 'C66': 71.8857594856572}, 'Pt': {'C11': 305.2046295775751, 'C12': 227.20314716315946, 'C13': 227.2031471630911, 'C33': 305.2046295775981, 'C44': 71.20763184256528, 'C66': 71.20763184255404}, 'Cr': {'C11': 577.4173484911852, 'C12': 122.93925749050601, 'C13': 122.93925749062915, 'C33': 577.4173484913239, 'C44': 84.39083446672848, 'C66': 84.39083446649782}, 'Mo': {'C11': 460.89619835642384, 'C12': 174.17488389616906, 'C13': 174.1748838962592, 'C33': 460.896198356311, 'C44': 87.98340051354462, 'C66': 87.98340051365753}, 'Ta': {'C11': 261.13084445878576, 'C12': 165.63623700379517, 'C13': 165.63623700375624, 'C33': 261.13084445863973, 'C44': 47.03914638790879, 'C66': 47.03914638783101}, 'V': {'C11': 333.59032091430805, 'C12': 123.35104957938036, 'C13': 123.35104957948653, 'C33': 333.5903209143214, 'C44': 18.426819802810034, 'C66': 18.426819802743907}, 'W': {'C11': 505.07462057646126, 'C12': 208.17653348334065, 'C13': 208.17653348325243, 'C33': 505.07462057664907, 'C44': 121.46171091690317, 'C66': 121.46171091690319}, 'Mg': {'C11': 63.047522240324206, 'C12': 28.31352788975207, 'C13': 20.274086623260615, 'C33': 72.45597050577291, 'C44': 17.05189512247541, 'C66': 17.335694359718183}, 'Ti': {'C11': 173.00104165368958, 'C12': 93.72524053775635, 'C13': 68.61850362886067, 'C33': 207.2573286238111, 'C44': 40.558009449317964, 'C66': 39.63755036573828}, 'Zr': {'C11': 141.4168740907924, 'C12': 81.36539940276177, 'C13': 62.175397155086294, 'C33': 137.3561106894711, 'C44': 21.257185432347427, 'C66': 29.982012625255983}}\n",
    "\n",
    "symbols = [\"Ag\", \"Al\", \"Au\", \"Cu\", \"Ni\", \"Pb\", \"Pd\", \"Pt\", \"Cr\", \"Mo\", \"Ta\", \"V\", \"W\", \"Mg\", \"Ti\", \"Zr\"]\n",
    "components = [\"C11\", \"C12\",\"C13\", \"C33\", \"C44\",  \"C66\"]\n",
    "\n",
    "fig, axs = plt.subplots(3, 2, figsize=(15, 15))\n",
    "plt.rcParams.update({'font.size': 15})\n",
    "\n",
    "ind = np.arange(len(symbols)) \n",
    "width = 0.2\n",
    "\n",
    "for i, component in enumerate(components):\n",
    "    dft_component_data = [dft_data[elem][component] if elem in dft_data and component in dft_data[elem] else 0 for elem in symbols]\n",
    "    nep_component_data = [nep_data[elem][component] if elem in dft_data and component in dft_data[elem] else 0 for elem in symbols]\n",
    "    eam_component_data = [eam_data[elem][component] if elem in eam_data and component in eam_data[elem] else 0 for elem in symbols]\n",
    "    \n",
    "    row = i // 2\n",
    "    col = i % 2\n",
    "\n",
    "    axs[row, col].bar(ind, nep_component_data, width, label='UNEP_v1', color = 'green')\n",
    "    axs[row, col].bar(ind - width, dft_component_data, width, label='DFT', color = 'purple')\n",
    "    axs[row, col].bar(ind + width, eam_component_data, width, label='EAM', color = 'red')\n",
    "    \n",
    "    axs[row, col].set_ylabel('GPa', fontsize=15)\n",
    "    axs[row, col].set_title('Comparison of {} Component Values'.format(component))\n",
    "    axs[row, col].set_xticks(ind)\n",
    "    axs[row, col].set_xticklabels(symbols, fontsize=15) \n",
    "    axs[row, col].legend()\n",
    "\n",
    "fig.patch.set_facecolor('white')\n",
    "plt.tight_layout()\n",
    "plt.savefig('elastic_constants.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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